The Custodial Era of UX: Cleaning Up After AI - NN/G

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August 28, 2026 2026-08-28
We are entering the custodial era of UX. In this era, AI makes it possible to generate content, prototype, vibe code, and build working features much faster than before. This newfound speed is valuable, but it also leads to UX debt when teams haven’t taken the time to understand, evaluate, or refine their AI output.
In many organizations, UX is therefore encountering work later in the process: a prototype already exists, an AI-generated feature has already been added, or a workflow is already in production. The work is less about “designing from a blank page” and more about “deciding what from AI to keep and what needs to change.” That is custodial work. Luckily, UX is good at bringing structure to chaos and evaluating what actually matters for users, and those skills are important when production moves faster than traditional UX processes.
You’ve probably experienced this type of cleanup work before. When teams move directly from an idea to a plausible-looking artifact and experiences are created through “vibes” or intuition, it’s easy to postpone foundational UX questions about user behaviors or whether features should exist at all. The result can be:
Learn how to structure UX research, standards, and design knowledge so AI can generate more usable, user-centered designs.
AI-generated or AI-assisted work isn’t inherently bad. The problem is that production has become cheaper than UX evaluation. It can now take less time to create an experience than to decide whether that experience is useful, usable, trustworthy, or coherent.
AI can generate 5 versions of a workflow in an afternoon, but deciding which one actually helps users requires understanding:
When evaluation does not keep pace with implementation, teams accumulate UX debt: the resulting experience may work well enough to demo or ship, but user confusion and degraded trust surface later. Shipping something quickly is not the same as creating something useful.
Some problems become apparent only when users try to use the feature in the context of their real lives, and the confusion, abandonment, support requests, or workarounds start to surface.
AI can also exacerbate issues unrelated to the technology itself. When teams communicate poorly, follow a broken process, or make abrupt, poorly informed decisions, flaws in the design get built and shipped more quickly. AI makes teams faster but also makes their messes more consequential.
Many organizations and teams are now running through a custodial pattern:
This pattern can occur even in organizations that value UX and normally involve it in product development. Because AI makes it so quick to build things, even these teams can find themselves with a working feature without taking the time to assess its value and usability.
In this environment, UX practitioners are:
The custodian role can feel frustrating, especially when UX is brought in only once the experience has become difficult to use. But this role can also create leverage by teaching organizations how to prevent the next mess. That involves three types of work:
UX practitioners who combine foundational UX expertise with practical AI literacy are well-positioned to carry out this type of work.
Simply asking teams to give up on using AI to generate prototypes and content is unlikely to work. If AI tools are available and fast, people will use them. UX cannot rely on teams giving up this technology entirely and reverting to standard, well-established user-centered processes.
Instead, UX needs to preserve the ability to provide design input while changing how and when this input is provided. Even if the original process changes, the questions it was meant to answer should still be addressed, even as production moves faster.
A working prototype can easily move the conversation from “Do we need this?” to “How soon can we ship this?” UX should help teams distinguish buildability from usefulness and recognize that shipping a feature quickly is not the same as improving the user experience.
When you receive an already built AI-generated feature, don’t start right away by polishing it. Triage it first and do a cost–benefit analysis. Ask yourself:
The most important question is still: “Does this improve the user’s experience?” Not: “Can we technically build it?”
Push back on refining features nobody needs. The outcome of triage is not always “redesign it.” You may keep a feature, simplify it, integrate it into other flows, postpone launching it, or remove it. Your work is to help the team decide what’s worth saving.
Even if you think that a feature may be worth pursuing, stay vigilant for any extras that add unneeded complexity and edit mercilessly. Messages that obscure the core value? Redundant controls? Duplicated workflows? All of these should be removed rather than improved. Look out for these common AI pitfalls and edit them out.
Questioning what gets built is ultimately a form of UX education, and triage should yield more than a decision; it should also produce a lesson for the team. Explain why the feature works or doesn’t, so the rest of the team learns how to apply the same UX criteria to the next idea. Over time, this builds shared judgment rather than leaving UX as the only group responsible for spotting problems. (And it may teach your teammates to generate better designs).
If AI enables the rapid production of prototypes and new systems, then UX must adapt its processes to the new rhythm. The answer is not to abandon users’ needs or to just trust the prototype. Instead, it’s to find ways to evaluate, triage, and discard prototypes more quickly.
Research doesn’t have to become a bottleneck for evaluating prototypes. Match the evidence to the risk involved: a low-risk feature may warrant quick user tests, a new high-stakes workflow may warrant